{
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  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "name": "train_birdview_vehicles.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "GI9KZ3F8TLSK"
      },
      "source": [
        "# EfficientDet Training On A Custom Dataset\n",
        "\n",
        "\n",
        "\n",
        "<table align=\"left\"><td>\n",
        "  <a target=\"_blank\"  href=\"https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/blob/master/tutorial/train_logo.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on github\n",
        "  </a>\n",
        "</td><td>\n",
        "  <a target=\"_blank\"  href=\"https://colab.research.google.com/github/zylo117/Yet-Another-EfficientDet-Pytorch/blob/master/tutorial/train_logo.ipynb\">\n",
        "    <img width=32px src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "</td></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "67-3S5_VTLSL"
      },
      "source": [
        "## This tutorial will show you how to train a custom dataset.\n",
        "\n",
        "## Please enable GPU support to accelerate on notebook setting if you are using colab.\n",
        "\n",
        "### 0. Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "90laRz20TLSN"
      },
      "source": [
        "!pip install pycocotools numpy==1.16.0 opencv-python tqdm tensorboard tensorboardX pyyaml webcolors matplotlib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "-R5C4DaETLSS"
      },
      "source": [
        "### 1. Prepare Custom Dataset/Pretrained Weights (Skip this part if you already have datasets and weights of your own)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "JmCQj3rhTLSS"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "if \"projects\" not in os.getcwd():\n",
        "  !git clone --depth 1 https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch\n",
        "  os.chdir('Yet-Another-EfficientDet-Pytorch')\n",
        "  sys.path.append('.')\n",
        "else:\n",
        "  !git pull\n",
        "\n",
        "# download and unzip dataset\n",
        "! mkdir datasets\n",
        "! wget https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/download/1.1/dataset_birdview_vehicles.zip\n",
        "! unzip -d datasets/ dataset_birdview_vehicles.zip\n",
        "\n",
        "# download pretrained weights\n",
        "! mkdir weights\n",
        "! wget https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/download/1.0/efficientdet-d0.pth -O weights/efficientdet-d0.pth\n",
        "\n",
        "# prepare project file projects/logo.yml\n",
        "# showing its contents here\n",
        "! cat projects/birdview_vehicles.yml"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "7Q2onXNZTLSV"
      },
      "source": [
        "### 2. Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "a-eznEu5TLSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66f20163-eaad-4b1f-a1b2-de3673315ac0"
      },
      "source": [
        "# consider this is a simple dataset, train head will be enough.\n",
        "! python train.py -c 0 -p birdview_vehicles --head_only True --lr 5e-3 --batch_size 32 --load_weights weights/efficientdet-d0.pth  --num_epochs 10 --save_interval 100\n",
        "\n",
        "# the loss will be high at first\n",
        "# don't panic, be patient,\n",
        "# just wait for a little bit longer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.06s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.02s)\n",
            "creating index...\n",
            "index created!\n",
            "[Warning] Ignoring Error(s) in loading state_dict for EfficientDetBackbone:\n",
            "\tsize mismatch for classifier.header.pointwise_conv.conv.weight: copying a param with shape torch.Size([810, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([18, 64, 1, 1]).\n",
            "\tsize mismatch for classifier.header.pointwise_conv.conv.bias: copying a param with shape torch.Size([810]) from checkpoint, the shape in current model is torch.Size([18]).\n",
            "[Warning] Don't panic if you see this, this might be because you load a pretrained weights with different number of classes. The rest of the weights should be loaded already.\n",
            "[Info] loaded weights: efficientdet-d0.pth, resuming checkpoint from step: 0\n",
            "[Info] freezed backbone\n",
            "Step: 33. Epoch: 0/10. Iteration: 34/34. Cls loss: 1.07845. Reg loss: 3.54955. Total loss: 4.62801: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 0/10. Classification loss: 1.90528. Regression loss: 5.18459. Total loss: 7.08987\n",
            "Step: 67. Epoch: 1/10. Iteration: 34/34. Cls loss: 0.94970. Reg loss: 2.79542. Total loss: 3.74512: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 1/10. Classification loss: 1.21661. Regression loss: 3.85998. Total loss: 5.07659\n",
            "Step: 99. Epoch: 2/10. Iteration: 32/34. Cls loss: 0.67331. Reg loss: 2.86182. Total loss: 3.53513:  91% 31/34 [00:36<00:01,  1.65it/s]checkpoint...\n",
            "Step: 101. Epoch: 2/10. Iteration: 34/34. Cls loss: 0.69130. Reg loss: 3.21338. Total loss: 3.90468: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 2/10. Classification loss: 0.82228. Regression loss: 3.44965. Total loss: 4.27193\n",
            "Step: 135. Epoch: 3/10. Iteration: 34/34. Cls loss: 0.63720. Reg loss: 2.93839. Total loss: 3.57558: 100% 34/34 [00:38<00:00,  1.14s/it]\n",
            "Val. Epoch: 3/10. Classification loss: 0.72073. Regression loss: 3.05390. Total loss: 3.77463\n",
            "Step: 169. Epoch: 4/10. Iteration: 34/34. Cls loss: 0.59181. Reg loss: 2.90764. Total loss: 3.49946: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 4/10. Classification loss: 0.67161. Regression loss: 3.52241. Total loss: 4.19402\n",
            "Step: 199. Epoch: 5/10. Iteration: 30/34. Cls loss: 0.59205. Reg loss: 2.68785. Total loss: 3.27990:  85% 29/34 [00:35<00:03,  1.65it/s]checkpoint...\n",
            "Step: 203. Epoch: 5/10. Iteration: 34/34. Cls loss: 0.60190. Reg loss: 2.72335. Total loss: 3.32526: 100% 34/34 [00:38<00:00,  1.14s/it]\n",
            "Val. Epoch: 5/10. Classification loss: 0.63244. Regression loss: 2.93657. Total loss: 3.56902\n",
            "Step: 237. Epoch: 6/10. Iteration: 34/34. Cls loss: 0.53837. Reg loss: 2.54831. Total loss: 3.08668: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 6/10. Classification loss: 0.63630. Regression loss: 2.88363. Total loss: 3.51993\n",
            "Step: 271. Epoch: 7/10. Iteration: 34/34. Cls loss: 0.58072. Reg loss: 2.56340. Total loss: 3.14412: 100% 34/34 [00:38<00:00,  1.13s/it]\n",
            "Val. Epoch: 7/10. Classification loss: 0.61607. Regression loss: 2.89553. Total loss: 3.51160\n",
            "Step: 299. Epoch: 8/10. Iteration: 28/34. Cls loss: 0.59194. Reg loss: 2.46366. Total loss: 3.05560:  79% 27/34 [00:34<00:04,  1.65it/s]checkpoint...\n",
            "Step: 305. Epoch: 8/10. Iteration: 34/34. Cls loss: 0.53251. Reg loss: 3.13931. Total loss: 3.67182: 100% 34/34 [00:38<00:00,  1.12s/it]\n",
            "Val. Epoch: 8/10. Classification loss: 0.62160. Regression loss: 2.80894. Total loss: 3.43054\n",
            "Step: 339. Epoch: 9/10. Iteration: 34/34. Cls loss: 0.54988. Reg loss: 2.55591. Total loss: 3.10579: 100% 34/34 [00:38<00:00,  1.12s/it]\n",
            "Val. Epoch: 9/10. Classification loss: 0.59771. Regression loss: 2.92351. Total loss: 3.52122\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U09nvLg_ss1v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "260b3813-149a-4b46-a9fd-c3c48d04172d"
      },
      "source": [
        "! python train.py -c 0 -p birdview_vehicles --head_only False --lr 1e-3 --batch_size 16 --load_weights last  --num_epochs 16 --save_interval 100"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.06s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.02s)\n",
            "creating index...\n",
            "index created!\n",
            "using weights logs//birdview_vehicles/efficientdet-d0_8_306.pth\n",
            "[Info] loaded weights: efficientdet-d0_8_306.pth, resuming checkpoint from step: 306\n",
            "Step: 344. Epoch: 4/16. Iteration: 69/69. Cls loss: 0.33619. Reg loss: 1.66370. Total loss: 1.99990: 100% 69/69 [00:47<00:00,  1.46it/s]\n",
            "Val. Epoch: 4/16. Classification loss: 0.76417. Regression loss: 4.01592. Total loss: 4.78009\n",
            "Step: 399. Epoch: 5/16. Iteration: 55/69. Cls loss: 0.33938. Reg loss: 1.30131. Total loss: 1.64069:  78% 54/69 [00:52<00:11,  1.27it/s]checkpoint...\n",
            "Step: 413. Epoch: 5/16. Iteration: 69/69. Cls loss: 0.22673. Reg loss: 1.30066. Total loss: 1.52739: 100% 69/69 [01:03<00:00,  1.08it/s]\n",
            "Val. Epoch: 5/16. Classification loss: 0.40379. Regression loss: 1.87583. Total loss: 2.27962\n",
            "Step: 482. Epoch: 6/16. Iteration: 69/69. Cls loss: 0.21152. Reg loss: 1.39837. Total loss: 1.60990: 100% 69/69 [01:03<00:00,  1.08it/s]\n",
            "Val. Epoch: 6/16. Classification loss: 0.28672. Regression loss: 1.53676. Total loss: 1.82348\n",
            "Step: 499. Epoch: 7/16. Iteration: 17/69. Cls loss: 0.19186. Reg loss: 0.96610. Total loss: 1.15795:  23% 16/69 [00:21<00:45,  1.16it/s]checkpoint...\n",
            "Step: 551. Epoch: 7/16. Iteration: 69/69. Cls loss: 0.20889. Reg loss: 0.89575. Total loss: 1.10465: 100% 69/69 [01:03<00:00,  1.08it/s]\n",
            "Val. Epoch: 7/16. Classification loss: 0.25191. Regression loss: 1.31228. Total loss: 1.56419\n",
            "Step: 599. Epoch: 8/16. Iteration: 48/69. Cls loss: 0.14847. Reg loss: 0.75740. Total loss: 0.90588:  68% 47/69 [00:46<00:17,  1.24it/s]checkpoint...\n",
            "Step: 620. Epoch: 8/16. Iteration: 69/69. Cls loss: 0.15360. Reg loss: 1.10270. Total loss: 1.25630: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 8/16. Classification loss: 0.24089. Regression loss: 1.33358. Total loss: 1.57447\n",
            "Step: 689. Epoch: 9/16. Iteration: 69/69. Cls loss: 0.10308. Reg loss: 0.68281. Total loss: 0.78588: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 9/16. Classification loss: 0.37497. Regression loss: 1.72728. Total loss: 2.10225\n",
            "Step: 699. Epoch: 10/16. Iteration: 10/69. Cls loss: 0.13121. Reg loss: 0.64150. Total loss: 0.77271:  13% 9/69 [00:15<01:12,  1.21s/it]checkpoint...\n",
            "Step: 758. Epoch: 10/16. Iteration: 69/69. Cls loss: 0.15039. Reg loss: 0.77006. Total loss: 0.92045: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 10/16. Classification loss: 0.21879. Regression loss: 1.21685. Total loss: 1.43563\n",
            "Step: 799. Epoch: 11/16. Iteration: 41/69. Cls loss: 0.10349. Reg loss: 0.65556. Total loss: 0.75905:  58% 40/69 [00:41<00:24,  1.20it/s]checkpoint...\n",
            "Step: 827. Epoch: 11/16. Iteration: 69/69. Cls loss: 0.12950. Reg loss: 0.70912. Total loss: 0.83861: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 11/16. Classification loss: 0.26531. Regression loss: 1.32277. Total loss: 1.58808\n",
            "Step: 896. Epoch: 12/16. Iteration: 69/69. Cls loss: 0.14511. Reg loss: 0.64932. Total loss: 0.79443: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 12/16. Classification loss: 0.25609. Regression loss: 1.23703. Total loss: 1.49312\n",
            "Step: 899. Epoch: 13/16. Iteration: 3/69. Cls loss: 0.09745. Reg loss: 0.61616. Total loss: 0.71361:   3% 2/69 [00:09<06:11,  5.55s/it]checkpoint...\n",
            "Step: 965. Epoch: 13/16. Iteration: 69/69. Cls loss: 0.10642. Reg loss: 0.58508. Total loss: 0.69150: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 13/16. Classification loss: 0.23048. Regression loss: 1.26382. Total loss: 1.49430\n",
            "Step: 999. Epoch: 14/16. Iteration: 34/69. Cls loss: 0.08973. Reg loss: 0.66329. Total loss: 0.75302:  48% 33/69 [00:35<00:29,  1.21it/s]checkpoint...\n",
            "Step: 1034. Epoch: 14/16. Iteration: 69/69. Cls loss: 0.06830. Reg loss: 0.50237. Total loss: 0.57067: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 14/16. Classification loss: 0.28616. Regression loss: 1.18964. Total loss: 1.47580\n",
            "Step: 1099. Epoch: 15/16. Iteration: 65/69. Cls loss: 0.07088. Reg loss: 0.45795. Total loss: 0.52882:  93% 64/69 [00:59<00:03,  1.28it/s]checkpoint...\n",
            "Step: 1103. Epoch: 15/16. Iteration: 69/69. Cls loss: 0.08287. Reg loss: 0.57496. Total loss: 0.65783: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 15/16. Classification loss: 0.27753. Regression loss: 1.14476. Total loss: 1.42229\n",
            "Step: 1172. Epoch: 16/16. Iteration: 69/69. Cls loss: 0.07603. Reg loss: 0.50745. Total loss: 0.58348: 100% 69/69 [01:03<00:00,  1.09it/s]\n",
            "Val. Epoch: 16/16. Classification loss: 0.26371. Regression loss: 1.12516. Total loss: 1.38887\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "05mjrGRETLSZ"
      },
      "source": [
        "### 3. Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "9yzNyaSxTLSZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6c26fad7-704a-422e-9fe1-f337690e3bf8"
      },
      "source": [
        "#get latest weight file\n",
        "%cd logs/birdview_vehicles\n",
        "weight_file = !ls -Art | grep efficientdet\n",
        "%cd ../..\n",
        "\n",
        "#uncomment the next line to specify a weight file\n",
        "#weight_file[-1] = 'efficientdet-d0_49_1400.pth'\n",
        "\n",
        "! python coco_eval.py -c 0 -p birdview_vehicles -w \"logs/birdview_vehicles/{weight_file[-1]}\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Yet-Another-EfficientDet-Pytorch/logs/birdview_vehicles\n",
            "/content/Yet-Another-EfficientDet-Pytorch\n",
            "running coco-style evaluation on project birdview_vehicles, weights /content/Yet-Another-EfficientDet-Pytorch/logs/birdview_vehicles/efficientdet-d0_16_1173.pth...\n",
            "loading annotations into memory...\n",
            "Done (t=0.02s)\n",
            "creating index...\n",
            "index created!\n",
            "100% 249/249 [00:14<00:00, 16.76it/s]\n",
            "Loading and preparing results...\n",
            "DONE (t=0.11s)\n",
            "creating index...\n",
            "index created!\n",
            "BBox\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=6.83s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.16s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.431\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.761\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.448\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.311\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.476\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.424\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.053\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.336\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.562\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.446\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.611\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "zhV3bNF3TLSc"
      },
      "source": [
        "### 4. Visualize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "uEDHMAIJTLSc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "2291ad23-f698-4709-d0cd-97d92733809c"
      },
      "source": [
        "import torch\n",
        "from torch.backends import cudnn\n",
        "\n",
        "from backbone import EfficientDetBackbone\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from efficientdet.utils import BBoxTransform, ClipBoxes\n",
        "from utils.utils import preprocess, invert_affine, postprocess\n",
        "\n",
        "compound_coef = 0\n",
        "force_input_size = None  # set None to use default size\n",
        "img_path = 'datasets/birdview_vehicles/val/1135.jpg'\n",
        "\n",
        "threshold = 0.2\n",
        "iou_threshold = 0.2\n",
        "\n",
        "use_cuda = True\n",
        "use_float16 = False\n",
        "cudnn.fastest = True\n",
        "cudnn.benchmark = True\n",
        "\n",
        "obj_list = [ 'large-vehicle', 'small-vehicle' ]\n",
        "\n",
        "# tf bilinear interpolation is different from any other's, just make do\n",
        "input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]\n",
        "input_size = input_sizes[compound_coef] if force_input_size is None else force_input_size\n",
        "ori_imgs, framed_imgs, framed_metas = preprocess(img_path, max_size=input_size)\n",
        "\n",
        "if use_cuda:\n",
        "    x = torch.stack([torch.from_numpy(fi).cuda() for fi in framed_imgs], 0)\n",
        "else:\n",
        "    x = torch.stack([torch.from_numpy(fi) for fi in framed_imgs], 0)\n",
        "\n",
        "x = x.to(torch.float32 if not use_float16 else torch.float16).permute(0, 3, 1, 2)\n",
        "\n",
        "model = EfficientDetBackbone(compound_coef=compound_coef, num_classes=len(obj_list),\n",
        "\n",
        "                             # replace this part with your project's anchor config\n",
        "                             ratios=[(0.7, 1.4), (1.0, 1.0), (1.5, 0.7)],\n",
        "                             scales=[2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\n",
        "\n",
        "model.load_state_dict(torch.load('logs/birdview_vehicles/'+weight_file[-1]))\n",
        "model.requires_grad_(False)\n",
        "model.eval()\n",
        "\n",
        "if use_cuda:\n",
        "    model = model.cuda()\n",
        "if use_float16:\n",
        "    model = model.half()\n",
        "\n",
        "with torch.no_grad():\n",
        "    features, regression, classification, anchors = model(x)\n",
        "\n",
        "    regressBoxes = BBoxTransform()\n",
        "    clipBoxes = ClipBoxes()\n",
        "\n",
        "    out = postprocess(x,\n",
        "                      anchors, regression, classification,\n",
        "                      regressBoxes, clipBoxes,\n",
        "                      threshold, iou_threshold)\n",
        "\n",
        "out = invert_affine(framed_metas, out)\n",
        "\n",
        "for i in range(len(ori_imgs)):\n",
        "    if len(out[i]['rois']) == 0:\n",
        "        continue\n",
        "    ori_imgs[i] = ori_imgs[i].copy()\n",
        "    for j in range(len(out[i]['rois'])):\n",
        "        (x1, y1, x2, y2) = out[i]['rois'][j].astype(np.int)\n",
        "        cv2.rectangle(ori_imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2)\n",
        "        obj = obj_list[out[i]['class_ids'][j]]\n",
        "        score = float(out[i]['scores'][j])\n",
        "\n",
        "        cv2.putText(ori_imgs[i], '{}, {:.3f}'.format(obj, score),\n",
        "                    (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                    (255, 255, 0), 1)\n",
        "\n",
        "        plt.imshow(ori_imgs[i])\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}